Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1611–1620 of 1718 papers

Title	Date	Tasks	Status
Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning	May 30, 2025	Dimensionality Reductionfeature selection	CodeCode Available
Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field Equilibria	Sep 28, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments	Feb 24, 2021	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available
Conversational AI for Positive-sum Retailing under Falsehood Control	May 1, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Learning to Schedule Communication in Multi-agent Reinforcement Learning	Feb 5, 2019	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets	Feb 21, 2022	ManagementMulti-agent Reinforcement Learning	CodeCode Available
Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy Optimization	Aug 13, 2023	LEMMAMuJoCo	CodeCode Available
Learning to Gather without Communication	Feb 21, 2018	Multi-agent Reinforcement LearningPosition	CodeCode Available
Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks	May 12, 2023	Data InteractionMulti-agent Reinforcement Learning	CodeCode Available
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning	Nov 7, 2024	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified